library(bigMap)
Load data
load('./P100K.RData')
Load results
load('./umap.RData')
load('./fitsne.RData')
load('./ptsne.RData')
ls()
## [1] "fitsne.list" "P100K" "ptsne.list" "umap.list"
hl-Correlation
pt-SNE
hlTable <- t(sapply(ptsne.list, function(m) summary(m$hlC)))
rownames(hlTable) <- sapply(ptsne.list, function(m) m$ppx$ppx)
knitr::kable(hlTable, caption = 'hl-Correlation') %>%
kable_styling(full_width = F)
hl-Correlation
|
|
Min.
|
1st Qu.
|
Median
|
Mean
|
3rd Qu.
|
Max.
|
|
50
|
0.0752411
|
0.0807705
|
0.0851543
|
0.0846177
|
0.0870846
|
0.0970239
|
|
500
|
0.3387809
|
0.3436912
|
0.3452630
|
0.3458974
|
0.3493087
|
0.3521840
|
|
5000
|
0.4628443
|
0.4711274
|
0.4755687
|
0.4749658
|
0.4795854
|
0.4847401
|
|
10000
|
0.4190242
|
0.4300974
|
0.4405758
|
0.4380868
|
0.4466536
|
0.4528383
|
Running Times
rTimes <- t(sapply(ptsne.list, function(m) (m$ppx$t[3] +m$t$ptsne[3])))
rTimes <- round(rTimes /60, 2)
knitr::kable(rTimes, caption = 'Computation times (min)') %>%
kable_styling(full_width = F)
Computation times (min)
|
elapsed
|
elapsed
|
elapsed
|
elapsed
|
|
55.24
|
52.33
|
71.29
|
100.92
|
Run on: Intel(R) Xeon(R) CPU E5-2650 v3 2.30GHz, 32Mb cache, 41 cores, 4GB/core RAM.